Prediction of Bending Beam Rheometer Test Outputs Using Artificial Neural Networks

The major objective of this study is to investigate the possibility of using Artificial Neural Networks in creating prediction models capable of estimating Bending Beam Rheometer outputs; namely creep stiffness, and m-value based on test temperature, modifier content; in our case waste vegetable oil...

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Bibliographic Details
Published in:Key Engineering Materials IX - 9th International Conference on Key Engineering Materials (9th ICKEM 2019) Vol. 821; p. 1
Main Authors: Aljarrah, Mohammad Fuad, Alshorman, Mohammad Emad, Khasawneh, Mohammad Ali, Al-Omari, Aslam Ali
Format: Book Chapter Journal Article
Language:English
Published: Zurich Trans Tech Publications 2019
Trans Tech Publications Ltd
Subjects:
Algorithms
Artificial neural networks
Back propagation
General Engineering & Project Administration
Materials
Neural networks
Prediction models
Rheometers
Stiffness
Testing time
Vegetable oils
Bending Beam Rheometer
Creep Stiffness
m-Value
Artificial Neural Networks
Waste Vegetable Oil
ISBN:9783035714807, 3035714800
ISSN:1013-9826, 1662-9795, 1662-9795
Online Access:Get full text
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Table of Contents:
  • 72.1 Introduction 72.2 Methodology 72.3 Results and Discussion 72.4 Conclusions References